class_docstr: Agent class which combines acting and learning.
  
  This provides an implementation of the `Actor` interface which acts and
  learns. It takes as input instances of both `acme.Actor` and `acme.Learner`
  classes, and implements the policy, observation, and update methods which
  defer to the underlying actor and learner.
  
  The only real logic implemented by this class is that it controls the number
  of observations to make before running a learner step. This is done by
  passing the number of `min_observations` to use and a ratio of
  `observations_per_step` := num_actor_actions / num_learner_steps.
  
  Note that the number of `observations_per_step` can also be in the range[0, 1]
  in order to allow the agent to take more than 1 learner step per action.
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code: def get_variables(
      self, names: Sequence[str]
  ) -> List[List[np.ndarray]]:
      return self._learner.get_variables(names)
  
file_path: acme/acme/agents/agent.py
imports: import math
  from typing import List, Optional, Sequence
  from acme import core
  from acme import types
  import dm_env
  import numpy
  import reverb
label_desc: Use image node_id 7 for calling the Agent obj's underlying member method code with example usage: obj.get_variables(names) and returns: self